In a power system, there are some key power devices such as a transformer, circuit breaker, power switch, recloser and so on. Once there is a fault in any of the power devices, it might result in a great influence on the whole power system, and sometimes it even might bring about severe accidents. Therefore, the kinds of power devices are important components that have substantial impacts on safe and reliable operations of the power system. That means it is crucial to find incipient faults in these power device as early as possible.
Device maintenance had been performed to find potential problems. Traditionally, the device maintenance is performed based on post-fault repair and/or on a periodical basis. However, such kinds of maintenance have their own drawbacks. For example, faults may occur between maintenance cycles, and in such a case, the maintenance can not reveal defects. On the other hand, the maintenance may also be performed even when there does not exist any defects, i.e., there might be an over-maintenance.
For the above reasons, there had been proposed to adopt on-line monitoring and prognosis to solve the above-mentioned problems. Generally, the on-line monitoring will collects condition data continuously and sends the collected data to prognosis module to perform further prognosis analysis. The maintenance may be performed based on results of prognosis analysis. For example, only if the results show that a power device has a defect with a high probability, maintenance is arranged so that it may be performed before the defect results in a real fault; and when the results show that the power device is operating in a normal condition, it may reduce the maintenance frequency to save man power and cost.
Currently, it has been proposed various schemes such as those based on probabilistic reasoning, decision tree, rough sets, information fusion, statistics, fuzzy mathematics and etc. However, all of these schemes can not provide a satisfactory effect and acceptable accuracy. Therefore, in the art, there is still a need for improving an on-line monitoring and prognosis.